Gartner says that predictive analytics is a mature technology yet only one company in eight is currently utilizing this ability to predict the future of sales, finance, production, and virtually every other area of the enterprise. What is the promise of predictive analytics and what exactly are they [types and uses of predictive analytics]? Good highlighting of main uses of predictive analytics in companies.

The Four Traps of Predictive Analytics

Magical thinking/ Starting at the Top/ Building Cottages, not Factories/ Seeking Purified Data. Good discussion. This short article in the Sloan Management Review is spot on, in my opinion. The way to develop good predictive analytics is to pick an area, indeed, pick the “low-handing fruit.” Develop workable applications, use them, improve them, broaden the scope. The “throw everything including the kitchen sink” approach of some early Big Data deployments is almost bound to fail. Flashy, trendy, but, in the final analysis, using “exhaust data” to come up with obscure customer metrics probably will not cut in the longer run.

Economic Issues

The Secular Stagnation Controversy

– discusses the e-book Secular Stagnation: Facts, Causes and Cures. The blogger Timothy Taylor points out that “secular” here has no relationship to lacking a religious context, but refers to the idea that market economies, or, if you like, capitalist economies, can experience long periods (decade or more) of desultory economic growth. Check the e-book for Larry Summer’s latest take on the secular stagnation hypothesis.

Here’s how much aid the US wants to send foreign countries in 2015, and why (INFOGRAPHIC

Deep Questions, effect of future events on past events

Video Friday – The Present Can Influence the Past?

August 29, 2014 Clive Jones 1 Comment

In forecasting, the common assumption is that the present influences the future, but the opposite does not occur.

Oh to be sure, one develops expectations and, yes, predictions which may influence present actions. But these are not realized, but projected. What actually occurs tomorrow, however, is not usually considered to directly influence what transpires today, particularly chance events. Thus, if Roger flips a coin tomorrow and it comes up heads, that is not supposed to have any material effect on physical processes occurring today.

But this turns out to happen at the level of quantum reality – in other words, at a more fundamental level of physical reality, as the quantum eraser experiment proves.

OK, it is a good idea to begin with the classic double slit experiment, as a lead-in. Here are two videos, one with a comic strip professor, and the second with Professor Brian Greene of Columbia University and several of his collegues.

So you immediately get into what I would call metaphysics – issues of whether consciousness can impinge on what is being observed, thus changing it.

Again, Professor Brian Greene on the double slit experiment, another narrative.

OK, so then there is the “quantum eraser.”

I’m still thinking about this. It’s profound, experimental metaphysics. Time is not what we think it is, just as space is not what it seems.

Quantum entanglement, incidentally, is what Einstein called “spooky action at a distance.”

accuracy of forecasts, long term forecasting, population forecasts

Population Forecasts, 2020 and 2030

August 28, 2014 Clive Jones

The United Nations population division produces widely-cited forecasts with country detail on a number of key metrics, such as age structure and median age.

The latest update (2012 revision) estimates 2010 base population at 6.9 billion persons, projecting global population at 7.7 billion and 8.4 billion in 2020 and 2030, respectively, in a medium fertility scenario.

The low fertility scenario projects 7.5 billion persons for 2020 and approximately 8.0 billion for 2030.

So, bottom line, global population is unlikely to peak during this forecast period to 2030, although it is likely to decline, under all fertility scenarios, for key players in the global economy – such as Japan and Germany.

Population decline is even possible, according to the 2012 revision, in a low fertility scenario for China, although not with higher birth rates, as indicated in the following chart.

Some rudimentary data analytics shows the importance of the estimate of median age in a country for its projected population growth in the 2012 revision.

For example, here is a scatter diagram of the median age within a country (horizontal or x-axis) and the percentage increase or decrease 2010-2030 in the medium fertility scenario of the UN projections. Thus, just to clarify, a 60 percent “percentage growth” on the vertical axis means 2030 population is 60 percent larger than the estimated base year 2010 population.

Note that a polynomial regression fits this scatter of points with a relatively high R². This indicates that the median age is negatively related to the projected population change for a country in this period with drop-offs in the earliest and oldest median ages in the population of countries.

Thus, in the first chart, Chinese growth includes the possibility of a decline over this period, and India’s does not. This is related to the fact that the median age of China in 2010 is estimated at 34.6 years, while the median age in 2010 in India is estimated at 25.5 years of age.

China and India, of course, are the world’s two most populous countries.

Here are some other interesting charts from the UN projections.

Russia, Japan, and Germany

The comparison for these countries is between the high fertility and low fertility scenarios. The middle fertility scenario lies pretty squarely between these curves for each nation.

Indonesia, Brazil, and Nigeria

Nigeria has the highest population growth rates for any larger country for this period, again because its 2010 median age is listed as around 18 years of age.

Accuracy of UN Population Forecasts

The accuracy of UN population forecasts has improved over the past several decades, with improved estimates of base population (See for example. Data quality and accuracy of UN Population Projections, 1950-1995). Needless to say forecasts for industrially developed counties usually have been better than for nations in the developing world.

Changes in migration account for significant errors in national population forecasts, as when a large contingent, some legal, some side-stepping legal immigration channels, came from Mexico and other Spanish-speaking areas “South of the Border,” changing birth patterns in the US from the early 1990’s to the years after 2000. In fact, during the early 1990’s, Census was predicting peak population for the US might occur as early as 2025. This idea went by the wayside, however, as younger, more fertile Hispanic families took their place in the country.

Current UN forecasts indicate US population should increase in the medium fertility scenario from 312 million to 338 million and 363 million, respectively, by 2020 and 2030.

Chinese economy, developed country forecast, developing country forecast, emerging market forecasts, long term forecasting, technology forecasting

2020 and 2030 – Forecasts and Projections

August 27, 2014 Clive Jones

I’d like to establish a context for discussing longer term forecasts, in this case to 2020 and 2030.

So, just below, I give you my take on 1990-2005. A lot happened that was unanticipated at the beginning of this period. One should expect, I think, the same to be true for 2015-2030.

Along those lines, I also suggest Big Picture factors that may come into play over the next fifteen or so years.

In coming posts, I want to summarize forecasts and projections I have seen for this period.

And I’m a little unusual in the technical forecasting community, since I’m equipped to do matrix programming, discuss boosting and bagging and so forth, and, on the other side of the aisle, weave together these stories and scenarios about process, causes, and factors. The quantitative is usually where I get paid, but, at the same time, I think it is easy to underestimate the benefit of trying to keep track of the Big Picture, the global dynamics, the political economy, and so forth.

1990-2005

The 1990’s rolled out with a nasty little recession in 1991 and voters throwing the first George Bush out of office, in favor of a clarinet-playing former Governor of Arkansas with a penchant for the ladies. Then, the United States experienced the longest period of economic prosperity since the 1960’s, fueled by the tech revolution and rise of the Internet. The breakup of the Soviet Union became official with democratic forms struggling to take root in Russia and former Soviet Republics. The US defense budget was cut about 40 percent from 1980 levels. Deregulation became a theme, and deregulation of telecoms led to burgeoning investments in telecom systems. The end of the decade saw the absurd Y2K problem, where details of computer clocks were supposed to stop everything at midnight, the turn of the century.

The New Millennium saw another recession in 2001, which was particularly sharp for the tech industry. Another Bush took the Presidency, after the Supreme Court intervened in the disputed General Election. Then there was 9/11 – September 11, 2001, with the destruction of the World Trade Center by large airliners being flown into the upper stories. This was a pivotal event. There was immediate surge in the military budget and in US military action in Afghanistan and then the invasion of Iraq, putatively because Saddam Hussein possessed “weapons of mass destruction.”

The US economy pretty much languished after the 2001-2002 recession, being stimulated to an extent by the rise in the defense budget, then by housing activity triggered by continued lowering of interest rates by the US Federal Reserve Bank under the redoubtable Alan Greenspan.

Another development that became especially noticeable after 2000 was the rise of China as a manufacturing and export power. The construction of the Shanghai skyline from the late 1990’s to the middle of the last decade was nothing less than stupendous.

The Importance of Technical Change

So what is important over a span of time? Are there underlying determinants?

I’ve got to believe technical change is an important element in historical process. If we take the fifteen year period sketched above, for example, a lot of the story is driven, at some level, by technical developments, especially in information technology (IT).

My favorite explanation of the collapse of the Soviet Union, for example, includes Silicon Valley as a key driver. The Soviet planned economy was a huge lumbering machine, compared to the nimble, change-oriented shops in the Valley, innovating new computer setups every few months. One immediate consequence was the US fighter aircraft came to totally dominate the old MIG planes, with their electronically guided missiles and tracking systems.

And to go on in this vein, focusing on the rise of US tech and then the movement of production to China is a strategic process for understanding the past couple of decades.

Big Picture Factors

Suffice it to say – new technology will be as much a driver of change in the next fifteen years, as it has been over the past fifteen.

Indeed, according to the futurist Ray Kurzweil, something called The Singularity stalks the human future. Perhaps around 2045, somewhat outside our forecast horizon in this discussion, technology will converge to completely outperform human intelligence. Commentators ranging from Stanislaus Ulam to Kurzweil believe that it is impossible to project human history beyond this point – hence the name.

Conventionally, this will involve biotechnology, computer technology, and robotics – but also could involve nanotechnology.

In any case, hefty doses of new technology may be necessary just to keep on a level course. I’m thinking, for example, of the diminishing effectiveness of antibiotics. So we have the evolution of “superbugs,” as well as the emergence of new epidemics through mutation or disease vectors jumping species lines. Ebola is a particularly gruesome example.

And while on technology, it is fair to observe that complex technologies just at or beyond the boundary of human control present deep challenges. Deep-sea oil drilling and the Gulf of Mexico oil spill, under British Petroleum, and the Fukishima nuclear disaster, still leaking radioactivity into the Pacific, are two examples.

Population or more generally demography is another Big Picture factor. Populations are aging in the United States, Europe, and Japan, but also in China. And global population continues to grow, possibly by another billion by 2030.

Climate change is another Big Picture factor.

The global climate is a complex, dynamic system. There is lots of noise in the discussion and uncertainties, such as whether there may be a cooling interval, as carbon dioxide and methane concentrations continue to rise globally. A number of studies commissioned by US and other intelligence agencies, though, highlight the potential for massive impacts from, say, basic changes in monsoon patterns in South Asia.

In terms of geopolitics, I suspect the shift in the economic center of gravity to somewhere along the Asian rim is another Big Picture development.

There are many relevant metrics. The proportion of global output produced by the United States, according to the World Economic Outlook (WEO) of the International Monetary Fund (IMF), will continue to diminuish, as Chinese growth in the worst case is projected to exceed levels of economic growth in the US and, certainly, in Europe.

Then, there is the issue of the US being the policeman of the world. At some point, the cost of maintaining a global span of military bases and force readiness for multiple theatres of action will weigh heavily on the US – as one could argue is already happening to some degree.

Challenges to the global dominance of the US dollar can be predicted, also, in the next fifteen years.

Sustainability

Whether any of the above “Big Picture” factors actually come into play by 2020 or 2030 is, of course, a speculation. But I think the basic technique of long term forecasting is to inventory possible influences like these. Then, you construct scenarios.

One thing appears certain. And that is there will be surprises.

In looking at forecasts for the next five to fifteen years, I also want to give thought to sustainability. Are there institutions and arrangements which could offer a backup to the various types of instabilities which could emerge?

And there is apparently an increasing chance of an increase in the general level of warfare, perhaps with linking of action in various theatres. I have to say, too, that I am poorly equipped to comment on these conflicts, although, as they ramp up, I attempt to learn more about the players and underlying dynamics.

I’ll be using this venue as a scratch-pad to record the projections of others and some thoughts I might have in response vis a vis 2020 and 2030.

Big Data, causes of deflation, Deep Questions, macroeconomic forecasting, real estate forecasts

Links – late August 2014

August 23, 2014 Clive Jones

Economics Articles, Some Theoretical, Some Applied

Who’s afraid of inflation? Not Fed Chair Janet Yellen At Jackson Hole, Yellen speech on labor market conditions states that 2 percent inflation is not a hard ceiling for the Fed.

Economist’s View notes a new paper which argues that deflation is simply unnecessary, because the conditions for a “helicopter drop” of money (Milton Friedman’s metaphor) are widely met.

Three conditions must be satisfied for helicopter money always to boost aggregate demand. First, there must be benefits from holding fiat base money other than its pecuniary rate of return. Second, fiat base money is irredeemable – viewed as an asset by the holder but not as a liability by the issuer. Third, the price of money is positive. Given these three conditions, there always exists – even in a permanent liquidity trap – a combined monetary and fiscal policy action that boosts private demand – in principle without limit. Deflation, ‘lowflation’ and secular stagnation are therefore unnecessary. They are policy choices.

Stiglitz: Austerity ‘Dismal Failure,’ New Approach Needed

US housing market loses momentum

Fannie Mae economists have downgraded their expectations for the U.S. housing market in the second half of this year, even though they are more optimistic about the prospects for overall economic growth.

How Detroit’s Water Crisis Is Part Of A Much Bigger Problem

“Have we truly become a society to where we’ll go and build wells and stuff in third world countries but we’ll say to hell with our own right here up under our nose, our next door neighbors, the children that our children play with?”

Economic harassment and the Ferguson crisis

According to .. [ArchCity Defenders] recent report .. the Ferguson court is a “chronic offender” in legal and economic harassment of its residents….. the municipality collects some $2.6 million a year in fines and court fees, typically from small-scale infractions like traffic violations…the second-largest source of income for that small, fiscally-strapped municipality….

And racial profiling appears to be the rule. In Ferguson, “86% of vehicle stops involved a black motorist, although blacks make up just 67% of the population,” the report states. “After being stopped in Ferguson, blacks are almost twice as likely as whites to be searched (12.1% vs. 7.9%) and twice as likely to be arrested.” But those searches result in the discovery of contraband at a much lower rate than searches of whites.

Once the process begins, the system begins to resemble the no-exit debtors’ prisons of yore. “Clients reported being jailed for the inability to pay fines, losing jobs and housing as a result of the incarceration, being refused access to the Courts if they were with their children or other family members….

“By disproportionately stopping, charging, and fining the poor and minorities, by closing the Courts to the public, and by incarcerating people for the failure to pay fines, these policies unintentionally push the poor further into poverty, prevent the homeless from accessing the housing, treatment, and jobs they so desperately need to regain stability in their lives, and violate the Constitution.” And they increase suspicion and disrespect for the system.

… the Ferguson court processed the equivalent of three warrants and $312 in fines per household in 2013.

Science

Astronauts find living organisms clinging to the International Space Station, and aren’t sure how they got there

A Mathematical Proof That The Universe Could Have Formed Spontaneously From Nothing

What caused the Big Bang itself? For many years, cosmologists have relied on the idea that the universe formed spontaneously, that the Big Bang was the result of quantum fluctuations in which the Universe came into existence from nothing.

Big Data Trends In 2014 (infographic – click to enlarge)

Chinese economy, ecommerce trends, technology forecasting

Video Friday – Ecommerce Trends

August 22, 2014 Clive Jones

Trends for 2014

Matt made this at the end of 2013, but it hits the mark for what we are seeing this year. It’s only two minutes! Part of a series called ’Two Minute Tuesdays’, but of course we are showing it on a Friday.

But a lot of what you find on ecommerce is US-centric. This leads to the question –

Should We Be Afraid of Alibaba?

Alibaba is bigger than Amazon and eBay combined, leading to an alarmist Bloomberg article earlier this month Alibaba’s IPO May Herald the End of U.S. E-Commerce Dominance

Ecommerce Trends in China

This YouTube video is a test run of a talk given May 2014 in China, and contains some material at the beginning which I consider to be superfluous – biography of the presenter, etc. However, if you get beyond that, there are a lot of key stats presented in the slides and presentation. Valuable.

consumer behavior, retail trends, sales forecasting, technology forecasting

Will Online Retail Cannibalize Brick-and-mortar Sales?

August 21, 2014 Clive Jones

Online retail or ecommerce is growing at three times the rate of retails sales generally (15 percent compared with 5 percent). And within online sales, mobile ecommerce is rocketing ahead by growth rates on the order of 25 percent per year in the US. Are these faster growing elements complementary to or cannibalizing conventional retail sales?

First, some stores – such as Blockbuster, Movie Gallery, Borders, and stores selling records and CD’s – are clearly casualties of Internet competition.

Other brick-and-mortar operations are following a multi-channel strategy, opening up online sales divisions parallel and in addition to their stores with goods on the shelves.

But the handwriting may be on the wall.

For one thing, in the 2013 holiday season, U.S. retailers saw approximately half the holiday foot traffic they experienced just three years ago.

And some of the foot traffic in brick-and-mortar stores is “showrooming” with practices highlighted in this infographic from Adweek (click to enlarge).

And it’s significant a pure-play ecommerce provider like Amazon has risen to one of the ten largest retailers in the United States, with 2013 sales of $44 billion.

While Amazon is still back in the pack (see Table below), its annual growth rate is unsurpassed.

Bottom line – the “fulfillment center” may become a growing trend.

People like to see the product, especially if it is a larger ticket item.

Interestingly, Amazon is now opening fulfillment centers in key urban markets. Other formerly brick-and-mortar stores may repurpose some of their floor area to warehousing and fulfillment of customer orders.

Recognize, however, that we’re talking about $3-4 trillion in retail sales in the US, and the game on the ground is likely to change relatively slowly – over five or ten years.

cell phone data analytics, mobile ecommerce, technology forecasting

Mobile e-commerce

August 20, 2014 Clive Jones

Mobile ecommerce is no longer just another means consumers use to buy products online. It’s now the predominant way buyers visit ecommerce sites.

And mobile applications are radically changing the nature of shopping. The Emeritus founder of comScore, for example, highlights the two aspects of mobile ecommerce,

Examples of m-Shopping, according to Internet Retailer, include –

Online consumers use their smartphones and tablets for many shopping-related activities. In Q2 2013, 57% of smartphone users while in a retailer’s store visited that retailer’s site or app compared with 43% who consulted another company’s site or app, comScore says. The top reason consumers consulted retailers’ sites or apps was to compare prices. Among those smartphone users who went to the same retailer’s site, 59% wanted to see if there was an online discount available, the report says. Similarly, among those who checked a different retailer’s site, 92% wanted to see if they could get a better deal on price.

Smartphone owners also used their devices while in stores to take a picture of a product (23%), text or call family or friends about a product (17%), and send a picture of a product to family and friends (17%).

According to Gian Fulgoni, “m-Buying” is the predominant way shoppers now engage with retail brands online in the US.

Growth, Adoption, and Use of Mobile E-Commerce explores patterns of mobile ecommerce with extensive data on eBay transactions.

One of the more interesting findings is that,

..adoption of the mobile shopping application is associated with both an immediate and sustained increase in total platform purchasing. The data also do not suggest that mobile application purchases are simply purchases that would have been made otherwise on the regular Internet platform.

The following chart illustrates this effect.

Finally. responsive web design seems to be a key to optimizing for mobile ecommerce.

Responsive web design is a process of making your website content adaptable to the size of the screen you are viewing it on. By doing so, you can optimise your site for mobile and tablet traffic, without the need to manage multiple templates, or separate content.

A/B and Bandit Testing, Big Data, website optimization

E-Commerce Apps for Website Optimization

August 19, 2014 Clive Jones

There are dozens of web-based metrics for assessing ecommerce sites, but in the final analysis it probably just comes down to “conversion” rate. How many visitors to your ecommerce site end up buying your product or service?

Many factors come into play – such as pricing structure, product quality, customer service, and reputation.

But real-time predictive analytics plays an increasing role, according to How Predictive Analytics Is Transforming eCommerce & Conversion Rate Optimization. The author – Peep Laja – seems fond of Lattice, writing that,

Lattice has researched how leading companies like Amazon & Netflix are using predictive analytics to better understand customer behavior, in order to develop a solution that helps sales professionals better qualify their leads.

Laja also notes impressive success stories, such as Macy’s – which clocked an 8 to 12 percent increase in online sales by combining browsing behavior within product categories and sending targeted emails by customer segment.

Google and Bandit Testing

I find the techniques associated with A/B or Bandit testing fascinating.

Google is at the forefront of this – the experimental testing of webpage design and construction.

Let me recommend readers directly to Google Analytics – the discussion headed by Overview of Content Experiments.

What is Bandit Testing?

Well, the Google presentation Multi-armed Bandits is really clear.

This is a fun topic.

So suppose you have a row of slot machine (“one-armed bandits”) and you know each machine has different probabilities and size of payouts. How do you decide which machine to favor, after a period of experimentation?

This is the multi-armed bandit or simply bandit problem, and is mathematically very difficult.

[The bandit problem] was formulated during the [second world] war, and efforts to solve it so sapped the energies and minds of Allied analysts that the suggestion was made that the problem be dropped over Germany, as the ultimate instrument of intellectual sabotage.

The Google discussion illustrates a Bayesian algorithm with simulations, showing that updating the probabilities and flow of traffic to what appear to be the most attractive web pages results, typically, in more rapid solutions that classical statistical experiments (generally known as A/B testing after “showroom A” and “showroom B”).

Suppose you’ve got a conversion rate of 4% on your site. You experiment with a new version of the site that actually generates conversions 5% of the time. You don’t know the true conversion rates of course, which is why you’re experimenting, but let’s suppose you’d like your experiment to be able to detect a 5% conversion rate as statistically significant with 95% probability. A standard power calculation1 tells you that you need 22,330 observations (11,165 in each arm) to have a 95% chance of detecting a .04 to .05 shift in conversion rates. Suppose you get 100 sessions per day to the experiment, so the experiment will take 223 days to complete. In a standard experiment you wait 223 days, run the hypothesis test, and get your answer.

Now let’s manage the 100 sessions each day through the multi-armed bandit. On the first day about 50 sessions are assigned to each arm, and we look at the results. We use Bayes’ theorem to compute the probability that the variation is better than the original2. One minus this number is the probability that the original is better. Let’s suppose the original got really lucky on the first day, and it appears to have a 70% chance of being superior. Then we assign it 70% of the traffic on the second day, and the variation gets 30%. At the end of the second day we accumulate all the traffic we’ve seen so far (over both days), and recompute the probability that each arm is best. That gives us the serving weights for day 3. We repeat this process until a set of stopping rules has been satisfied (we’ll say more about stopping rules below).

Figure 1 shows a simulation of what can happen with this setup. In it, you can see the serving weights for the original (the black line) and the variation (the red dotted line), essentially alternating back and forth until the variation eventually crosses the line of 95% confidence. (The two percentages must add to 100%, so when one goes up the other goes down). The experiment finished in 66 days, so it saved you 157 days of testing.

This Figure 1 chart is as follows.

This is obviously just one outcome, but running this test many times verifies that in a majority of cases, the Google algorithm results in substantial shortening of test time, compared with an A/B test. In addition, if actual purchases are the meaning of “conversion” here, revenues are higher.

This naturally generalizes to any number of “arms” or slot machines.

Apparently, investors have put nearly $200 million in 2014 into companies developing predictive apps for ecommerce.

And, on the other side of the ledger, there are those who say that the mathematical training of people who might use these apps is still sub-par, and that the full potential of these techniques may not be realized in many cases.

The deeper analytics of the Google application is fascinating. It involves Monte Carlo simulation to integrate products of conditional and prior distributions, after new data comes in.

My math intuition, such as it is, suggests that this approach has wider applications. Why could it not, for example, be utilized for new products, where there might be two states, i.e. the product is a winner (following similar products in ramping up) or a loser? It’s also been used in speeding up health trials – an application of Bayesian techniques.

Top graphic from the One Hour Professor

IT market analysis, predictive analytics, technology forecasting

e-commerce and Forecasting

August 18, 2014 Clive Jones

The Census Bureau announced numbers from its latest e-commerce survey August 15.

The basic pattern continues. US retail e-commerce sales increased about 16 percent on a year-over-year basis from the second quarter of 2013. By comparison, total retail sales for the second quarter 2014 increased just short of 5 percent on a year-over-year basis.

As with other government statistics relating to IT (information technology), one can quarrel with the numbers (they may, for example, be low), but there is impressive growth no matter how you cut it.

Some of the top e-retailers from the standpoint of clicks and sales numbers are listed in Panagiotelis et al. Note these are sample data, from comScore with the totals for each company or site representing a small fraction of their actual 2007 online sales.

Forecasting Issues

Forecasting issues related to e-commerce run the gamut.

Website optimization and target marketing raise questions such as the profitability of “stickiness” to e-commerce retailers. There are advanced methods to tease out nonlinear, nonnormal multivariate relationships between, say, duration and page views and the decision to purchase – such as copulas previously applied in financial risk assessment and health studies.

Mobile e-commerce is a rapidly growing area with special platform and communications characteristics all its own.

Then, there are the pros and cons of expanding tax collection for online sales.

All in all, Darrell Rigby’s article in the Harvard Business Review – The Future of Shopping – is hard to beat. Traditional retailers generally have to move to a multi-channel model, supplementing brick-and-mortar stores with online services.

I plan several posts on these questions and issues, and am open for your questions.

Top graphic by DIGISECRETS

Business Forecasting

Monthly Archives: August 2014

Links – Labor Day Weekend